The advent of improved pattern recognition procedures in recent years has opened a plethora of applications for this technique. Among these applications are medical diagnoses, biological categorization, financial market prediction, credit card fraud detection, retail transactions, insurance applications, and personal identification.
Statistical pattern recognition is theoretically well-founded when based on Bayes' decision rule. In practical applications, however, Bayes' decision rule is difficult to implement because real-world data is complex and multi-dimensional. This causes the amount of training data for statistical pattern recognition to rise exponentially with each additional data dimension.
It is well known that the dimension of real-life data can be reduced because these types of data often are highly redundant, and many of its components are irrelevant. The present invention addresses these issues and provides a statistical pattern recognition method that overcomes these problems through a combination of processes.
It is therefore an object of the present invention to provide a method of statistical pattern recognition that can identify relevant feature components.
It is another object of the present invention to provide a statistical pattern recognition method that capable of reducing the computer time required to run pattern recognition processes.
Additional objects, advantages and novel features of the invention will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following or may be learned by practice of the invention. The objects and advantages of the invention may be realized and attained by means of the instrumentalities and combinations particularly pointed out in the appended claims.